The DAG sequencing task we consider is an omnipresent stage in compiler workflows, which usually also include such tasks as device assignment and operations fusion [1]}. In such a setting, jointly optimizing these tasks to reduce the run time of a certain workflow (such as the forward pass of a Neural Net) is a common objective, which in [2]}, [1]} is tackled with ML methods.
In this work we focus on the task of minimizing the peak local memory usage during execution, which does not require a performance model or simulator as well as being relevant to applications on edge devices [4]}. In  [2]}, the ML solution leans on an existing genetic algorithm, whilst our solution is end-to-end, much like that proposed in  [1]}. Another characteristic of the solution proposed in [1]} is the idea of interpolating between AR and NAR via an iterative refinement scheme, in which sequences are generated in one pass but subsequently refined during an user-defined number of subsequent passes; conversely, we generate all our sequences in a single pass.
